Neuro-fuzzy Inference System Technique Research Articles

Random forest regression on pullout resistance of a pile

AbstractThis research aims to study the pullout resistance of a helical pile using three methods of machine learning techniques, which are: random forest regression, support vector regression, and adaptive neuro-fuzzy inference system, based on experimental results of a helical pile. The performance of these three techniques has been d compared and the results show that random forest algorithm has best performance than neuro-fuzzy inference system and support vector technique. The results show that machine learning considered a good tool in terms of estimating the pullout resistance of helical piles in the soil.

Propelling precision of longitudinal vibration mitigation in ship propeller shafts through advanced nonlinear intelligent semi-active control leveraging adaptive neuro-fuzzy inference system with linear quadratic regulator

This study presents a pioneering method to address longitudinal vibrations in ship propeller shafts by integrating an intelligent semi-active control system. By leveraging Adaptive Neuro-Fuzzy Inference System (ANFIS) and Linear Quadratic Regulator (LQR) techniques, the proposed system offers a novel solution to a longstanding challenge in maritime engineering. The efficacy of the semi-active control system is assessed through two complementary analyses: frequency domain analyses and Percentage Reduction Index (PRI) values. The investigation reveals substantial PRI values, ranging from 24.68% to 46.87%, indicating notable enhancements in vibration control with the adoption of semi-active technology. Furthermore, frequency domain analyses demonstrate significant advantages, with a peak reduction of 68.21% observed at 750 r/min. To rigorously evaluate the statistical significance of these improvements, paired t-tests were conducted between the semi-active control system and passive strategies. The results confirm a statistically significant difference in vibration reduction ( p < .05) between the two systems. Specifically, for Percentage Reduction Index Values, the t test yielded a p-value of .023, while for Frequency Domain Analyses, the t-statistic significantly exceeded the critical value (t ≈ 8.09, tcritical ≈ 2.571). Both data sets provide compelling evidence to reject the null hypothesis, indicating a significant difference in vibration reduction between the semi-active control system and passive strategies. The PRI values consistently demonstrate superior performance for the semi-active control system, highlighting its effectiveness in mitigating vibrations. These findings validate the effective ness of the system. These results show the practical implications of implementing semi-active control systems in maritime engineering, offering enhanced safety, fuel efficiency, and operational reliability in ship propeller shaft applications.

Re–operating the Bhumibol and Sirikit Dams Using Hybrid Neuro–Fuzzy Technique to Solve the Water Scarcity and Flooding Problems in the Chao Phraya River Basin

The decision support system to reservoir re–operation using Artificial Intelligence has been broadly studied and proven in term of the operational performances for both single and multiple reservoir system, this study applied Adaptive Neuro Fuzzy Inference System (ANFIS) technique for reservoir re–operation in Chao Phraya River Basin aiming to reduce water scarcity and flooding problems in the central region of Thailand. ANFIS is an integrated approach in which neural networks are utilized to enhance the fuzzy inference system and create fuzzy “IF–Then” reservoir operational guidelines with proper membership functions for reservoir re–operation. In this study, ANFIS operating rules were trained using two different datasets; long–term dataset (scenario 1) and water year–based dataset (scenario 2). It is revealed that the extent of yearly water deficit in critical dry years are totally reduced to nearly zero when re–operating with ANFIS operation rules, except in the year 2012. However, the yearly water deficit in year 2012 is also substantially reduced from 504 MCM by the current operation to 127 and 119 MCM for scenario 1 and scenario 2, respectively when two scenarios of ANFIS–based reservoir re–operation model were performed. Moreover, considerable total amount of spilled water from BB and SK Dams is definitely declined to 0 and 37 MCM in years 2002 and 2011, respectively when water year–based ANFIS model was implemented. In addition, it is expressed that average water storages of two main dams obtained from two scenarios of ANFIS model are substantially increased up to +6.08% and +6.94% for BB Dam and +0.09% and +1.62% for SK Dam in comparison with the current operation. This signifies that supplying water from dams to meet the target water demand through adaptive fuzzy–rules can be well handled and flooded water can be minimized.

Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development

In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional laboratory methods for obtaining CBR values are time-consuming and labor-intensive, prompting the exploration of novel computational strategies. This paper illustrates the development and application of machine learning techniques—multivariate linear regression (MLR), artificial neural networks (ANN), and the adaptive neuro-fuzzy inference system (ANFIS)—to indirectly predict the CBR based on the soil type, plasticity index (PI), and maximum dry density (MDD). Our study analyzed 2191 soil samples for parameters including PI, MDD, particle size distribution, and CBR, leveraging theoretical calculations and big data analysis. The ANFIS demonstrated superior performance in CBR prediction with an R2 value of 0.81, surpassing both MLR and ANN. Sensitivity analysis revealed the PI as the most significant parameter affecting the CBR, carrying a relative importance of 46%. The findings underscore the potent potential of machine learning and neuro-fuzzy inference systems in the sustainable management of non-renewable urban resources and provide crucial insights for urban planning, construction materials selection, and infrastructure development. This study bridges the gap between computational techniques and geotechnical engineering, heralding a new era of intelligent urban resource management.

Enhanced prediction of parking occupancy through fusion of adaptive neuro-fuzzy inference system and deep learning models

While predicting parking occupancy is crucial for managing urban congestion, existing models often exhibit gaps in accuracy, uncertainty handling, and integration potential. This study introduces an innovative combination of adaptive neuro-fuzzy inference system (ANFIS) and deep learning (DL) techniques to address these shortcomings. Specifically, ANFIS is utilized for its proficiency in uncertainty representation via fuzzy set theory, whereas DL models excel in automatic feature learning, non-linear modeling, and identifying long-term dependencies in time-series parking data. By integrating ANFIS with recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), we formulated the ANFIS-RNN, ANFIS-LSTM, and ANFIS-GRU fusion models, testing them on real-world parking datasets. Subsequent experiments highlighted the dominance of these fusion models over individual and benchmark counterparts. ANFIS-RNN achieved a 30.61% improvement in MSE, 16.70% in RMSE, 21.21% in MAE, 21.58% in MAPE, and a 1.03% elevation in R2 over the standalone RNN. The ANFIS-LSTM surpassed LSTM by 34.04% in MSE, 18.76% in RMSE, 26.16% in MAE, 27.71% in MAPE, with a 1.04% R2 increment. ANFIS-GRU exceeded GRU metrics by 27.54% in MSE, 14.85% in RMSE, 19.27% in MAE, 20.01% in MAPE, and boosted R2 by 1.03%. These outcomes underline the potential of integrated models in refining prediction precision. By leveraging the combined strengths of ANFIS and DL, this research offers a significant leap in parking occupancy forecasting. Its implications extend to data-centric urban planning and traffic regulation, marking a pivotal step for future endeavors in hybrid predictive modeling incorporating soft computing and deep learning paradigms.

Comparison of hybrid RNA-based models to predict the degradation and mineralization of the microcontaminant hormone 17β-estradiol

New alternatives for effluent decontamination, such as electrochemical oxidation, are being developed to provide adequate removal of endocrine disruptors such as 17β-estradiol in wastewater. In this study, data-driven models of response surface methodology, artificial neural networks, wavelet neural networks, and adaptive neuro-fuzzy inference system will be used to predict the degradation and mineralization of the microcontaminant hormone 17β-estradiol through an electrochemical process to contribute to the treatment of effluent containing urine. With the use of different statistical criteria and graphical analysis of the correlation between observed and predicted data, it was possible to conduct a comparative analysis of the performances of the data-driven approaches. The results point to the superiority of the adaptive neuro-fuzzy inference system (correlation coefficient, R2, ranged from 0.99330 to 0.99682 for TOC removal and from 0.95330 to 0.99223 for the degradation of the hormone 17β-estradiol) techniques over the others. The remaining results obtained with the other metrics are consistent with this analysis.

Quasi-oppositional artificial algae optimization with adaptive neuro fuzzy inference based maximum power point tracking for PV systems

Because of environmental issues and energy crises, significant attention has been received in the domain of renewable and clean energy systems. Solar energy is the most effective source of renewable energy technologies. Recently, photovoltaic (PV) system have become common in grid-linked applications and plays a vital part in power production. MPPT algorithms enable PV systems to capture the maximum available power from the solar panels, regardless of variations in solar irradiance, temperature, and other environmental factors. By continuously tracking the MPP, MPPT techniques ensure that the PV system operates at its highest efficiency, resulting in increased energy harvesting and improved overall performance. Meanwhile, the frequent modifications in irradiance and temperature pose a major challenging issue which can be resolved by the use of artificial intelligence MPPT methodologies like artificial neural networks (ANN), fuzzy logic (FL), and metaheuristics systems. In this aspect, this work presents a new quasi-oppositional artificial algae optimization (QOAAO) with an adaptive neuro-fuzzy inference system (ANFIS) technique, named QOAAO-ANFIS for maximum efficiency MPPT technique for minimizing the present ripple and power oscillations over the MPP. The presented QOAAO-ANFIS model mainly depends upon the integration of the ANFIS and QOHOA techniques. In addition, the presented QOAAO-ANFIS model involves optimal MF selection of the ANFIS model to estimate the irradiation level and compute PV voltage equivalent to maximal power point. The QOAAO model can be utilized for enhancing the optimization process of membership function variables under varying conditions and awareness of global optima. The simulation result analysis of the QOAAO-ANFIS model takes place in terms of different evaluation measures. Extensive comparative results reported the better performance of the QOAAO-ANFIS model with maximum tracking efficiency of 99.89% and a minimum convergence time of 13.51 ms.

Modeling the concentration of suspended particles by fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) techniques: A case study in the metro stations

Background: Today, the usage of artificial intelligence systems and computational intelligence is increasing. This study aimed to determine the fuzzy system algorithms to model and predict the amount of air pollution based on the measured data in subway stations. Methods: In this study, first, the effective variables on the concentration of particulate matter were determined in metro stations. Then, PM2.5, PM10, and total size particle (TSP) concentrations were measured. Finally, the particles’ concentration was modeled using fuzzy systems, including the fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS). Results: It was revealed that FIS with modes gradient segmentation (FIS-GS) could predict 76% and ANFIS-FCM with modes of clustering and post-diffusion training algorithm (CPDTA) could predict 85% of PM2.5, PM10, and TSP particle concentrations. Conclusion: According to the results, among the models studied in this work, ANFIS-FCM-CPDTA, due to its better ability to extract knowledge and ambiguous rules of the fuzzy system, was considered a suitable model.

Using adaptive neuro-fuzzy inference system and imperialist competitive algorithm for leak detection in pipe networks

Finding the position and quantity of leakage in water distribution networks (WDNs) is challenging in cities with old water pipelines. Previous studies proposed physical and data-driven techniques to find the location and quantity of leakage in WDNs. Most approaches rely on large sample of measurements from the WDN which makes them impractical. We propose an efficient model incorporating Adaptive Neuro-Fuzzy Inference System (ANFIS) and Imperialist Competitive Algorithm (ICA) techniques, to reduce the number of required samples. ANFIS approximates the locations and quantities of leakage, while the ICA corrects its estimations. Due to nonlinearity, the application of ICA alone results in long run times, while ANFIS reduces the number of decision variables for ICA, and hence the convergence rate improves. In other words, we reduce the search space leading to reduced computational time and improved accuracy. Results show that the normalized predicted leakage values had a maximum error rate of 10%.

Statistical computation and artificial neural algorithm modeling for the treatment of dye wastewater using mucuna sloanei as coagulant and study of the generated sludge

Adaptive neuro-fuzzy inference system and response surface methodology techniques were used to predict coagulation-flocculation parameters needed to remove color and COD effectively and produce acceptable sludge using Mucuna sloanei at 298 K from a dye wastewater. The variables input to the network were coagulant dosage (1000, 1400 and 1800 mg/L), solution pH (2, 6 and 10) and stirring time (5, 15 and 25 min). Coefficient of determination, R2 and root mean square, RMSE were used to evaluate the adequacy and predictive relevance of the two techniques. Color removal model indicators are R2 0.9823, RMSE 0.2599 and R2 0.8616, RMSE 21.403 for ANFIS and RSM, respectively; COD removal indicators are respectively, R2 0.9752, RMSE 0.2009, and R2 0.9741, RMSE 0.8118 for ANFIS and RSM; while sludge volume index model indicators are R2 0.9950, RMSE 0.2341, and R2 0.9930, RSME 2.1436, for ANFIS and RSM, respectively. With a limited set of data, the generated models produced idealized findings and were proven to be useful for forecasting color and COD elimination, and SVI. Nevertheless, the ANFIS model is clearly favored because of greater R2 values and lower RMSE.

Estimation of properties of ultra great workability concrete by optimized ANFIS models

The mechanical properties of the ultra-great workability concrete (UGWC) are deeply related to the weights of components, curing period and condition, and occasionally property of admixtures. This study aimed to appraise the usefulness of the adaptive neuro-fuzzy inference system (ANFIS) technique for forecasting the compressive strength of UGWC and enhancing the accuracy of the literature. To outline the forecasting process, two improved ANFIS were suggested, in which determinative variables of them were determined by metaheuristic algorithms named imperialist competitive algorithm (ICA) and multi-verse optimizer (MVO) algorithms. For this purpose, 170 data samples were collected from published literature separated accidentally for the train and test phase. The calculated performance criteria for proposed ANFIS models demonstrate that both ICA-ANFIS and MVO-ANFIS models can result in justifiable workability for fc of the UGWC prediction procedure. The MVO-ANFIS model could outperform ICA-ANFIS regarding all criteria. For instance, the value of R2 and VAF for the ICA-ANFIS model are roughly smaller than the MVO-ANFIS model, at 0.9012 and 90% in the training dataset and 0.8973 and 89% in the testing stage, respectively. While the best values of criteria have belonged to the MVO-ANFIS model, with R2 at 0.937 and 0.944 for the train and test phases, respectively. Overall, the hybrid MVO-ANFIS model can obtain higher workability than ICA-ANFIS and literature (R2 at 0.801), where causes are recognized as the proposed model.

Adaptive Neuro-Fuzzy Inference System (ANFIS) Method for Developing a Decision Support System for Determining Landslide Susceptibility

Landslide catastrophes are one of the disasters that frequently occur in Indonesia owing to the weather and climatic features, regional terrain, and geological formations that make this nation prone to landslides. The primary goal of this research is to compare the application of the fuzzy logic technique and the adaptive neuro-fuzzy inference system (ANFIS) approach to landslide detection sensors based on prior research in order to identify landslide-prone locations more easily. The Adaptive Neuro-Fuzzy Inference System (ANFIS) technique analyzes the landslide area using three factors. Rainfall, land slope, and soil moisture are examples of these factors. This variable is used to assess the area's level of vulnerability to landslides: very safe, relatively safe, relatively potential, potential, and very potential. In the study, each piece of data is subjected to a training and testing procedure to identify landslide vulnerability, with the factors and weighting methods aligned with current government standards. This study compares the rules outcomes to those of past studies as well as the system results. Based on the studies findings, it can be stated that the decision support system for the degree of landslide vulnerability utilizing the ANFIS approach is superior to the fuzzy logic method, with an accuracy rate of 86.21%.

Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq

Estimating the amount of solar radiation is very important in evaluating the amount of energy that can be received from the sun for the construction of solar power plants. Using machine learning tools to estimate solar energy can be a helpful method. With a high number of sunny days, Iraq has a high potential for using solar energy. This study used the Wavelet Artificial Neural Network (WANN), Wavelet Support Vector Machine (WSVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques to estimate solar energy at Wasit and Dhi Qar stations in Iraq. RMSE, EMA, R2, and IA criteria were used to evaluate the performance of the techniques and compare the results with the actual measured value. The results showed that the WANN and WSVM methods had similar results in solar energy modeling. However, the results of the WANN technique were slightly better than the WSVM technique. In Wasit and Dhi Qar stations, the value of R2 for the WANN and WSVM methods was 0.89 and 0.86, respectively. The value of R2 in the WANN and WSVM methods in Wasit and Dhi Qar stations was 0.88 and 0.87, respectively. The ANFIS technique also obtained acceptable results. However, compared to the other two techniques, the ANFIS results were lower, and the R2 value was 0.84 and 0.83 in Wasit and Dhi Qar stations, respectively.

Optimal Fuzzy Logic Enabled Intrusion Detection for Secure IoT-Cloud燛nvironment

Recently, Internet of Things (IoT) devices have developed at a faster rate and utilization of devices gets considerably increased in day to day lives. Despite the benefits of IoT devices, security issues remain challenging owing to the fact that most devices do not include memory and computing resources essential for satisfactory security operation. Consequently, IoT devices are vulnerable to different kinds of attacks. A single attack on networking system/device could result in considerable data to data security and privacy. But the emergence of artificial intelligence (AI) techniques can be exploited for attack detection and classification in the IoT environment. In this view, this paper presents novel metaheuristics feature selection with fuzzy logic enabled intrusion detection system (MFSFL-IDS) in the IoT environment. The presented MFSFL-IDS approach purposes for recognizing the existence of intrusions and accomplish security in the IoT environment. To achieve this, the MFSFL-IDS model employs data pre-processing to transform the data into useful format. Besides, henry gas solubility optimization (HGSO) algorithm is applied as a feature selection approach to derive useful feature vectors. Moreover, adaptive neuro fuzzy inference system (ANFIS) technique was utilized for the recognition and classification of intrusions in the network. Finally, binary bat algorithm (BBA) is exploited for adjusting parameters involved in the ANFIS model. A comprehensive experimental validation of the MFSFL-IDS model is carried out using benchmark dataset and the outcomes are assessed under distinct aspects. The experimentation outcomes highlighted the superior performance of the MFSFL-IDS model over recent approaches with maximum accuracy of 99.80%.

Reliability analysis and ANFIS computation for multi-server redundant machining system with the generalized triadic policy

The intent of this research is to have discussions about the transient behavior of a multi-server redundant machining system with a generalized Triadic control policy. Furthermore, the system facilitates a multiple working vacation policy for servers. The relevant rates of the associated birth-death process are chosen in order to frame the differential-difference equations for the system. Using the fourth order Runge-Kutta technique, time-dependent probabilities of the states are derived. Some performance metrics of system such as the average number of failed machines, average waiting time of failed machines, and reliability function are established using the states probabilities. Graphical and tabular representations demonstrate the behavior of these metrics with respect to a variety of system characteristics. Statistical hypothesis testing has been employed to compare the outcomes presented in tabular format. Beside these, numerical findings of the Runge-Kutta method are compared to those delivered by an adaptive neuro-fuzzy inference system (ANFIS) technique.

Analysis of the S-ANFIS Algorithm for the Detection of Blood Infections Using Hybrid Computing

Environment and climate change have caused a rise in a wide range of diseases and infections. In countries where overpopulation is a problem, many infections spread severely. The main focus of this paper is the detection and identification of blood diseases. An automated system that examines all potential diseases using patient information and data is needed to deal with unpredictable circumstances. Having an automated and intelligent system that evaluates the reports and counsels doctors in any other area or nation is a demand of the time. The same solutions can be identified by the proposed system. To apply the adaptive neuro-fuzzy inference system (ANFIS) and related techniques to predict chronic diseases early, the authors have gone through various existing models and case studies on diabetics and other patients. The proposed approach, called S-ANFIS which is using the hybrid approach, is based on ANFIS and includes content curation and intelligence analysis in addition to comparison with current models. As a result, the suggested model outperforms other approaches in terms of disease prediction accuracy, with a score of 88.6%.

Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques

In order to provide an effective tool for the simulation of wastewater treatment plant performance and control, a reliable model is essential. In the present study, two different artificial intelligence (AI) models; Adaptive Neuro-Fuzzy inference system (ANFIS), and a classical multi-linear regression analysis (MLR) were applied for predicting the performance of Abuja wastewater treatment plant (AWWTP), in terms of Conductivity, pH, Iron content, BOD, COD, TSS and TDS. The daily data were obtained from Abuja Wastewater treatment plant, for this purpose, single and ensemble models were employed to compare and improve the prediction performance of the plant. The obtained result of single models proved that, MLR model provides an effective analysis in comparison to the other single model. The result showed that, conductivity influences the performance and efficiency of the water treatment plant by an increased efficiency performance of AI modelling up to 99.6% testing phase and 6.8% Error value of same phase. This shows that MLR model was more robust and reliable method for predicting the Abuja WWTP performance.

Reliability Analysis of Gravity Retaining Wall Using Hybrid ANFIS

Gravity retaining walls are a vital structure in the area of geotechnical engineering, and academicians in earlier studies have conveyed substantial uncertainties involved in calculating the factor of safety against overturning, using a deterministic approach. Hence, to enhance the accuracy and eliminate the uncertainties involved, artificial intelligence (AI) was used in the present research. The main aim of this study is to propose a high-performance machine learning (ML) model to determine the factor of safety (FOS) of gravity retaining walls against overturning. The projected methodology included a novel hybrid machine learning model that merged with an adaptive neuro-fuzzy inference system (ANFIS) and meta-heuristic optimization techniques (particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FFA) and grey wolf optimization (GWO)). In this research, four hybrid models, namely ANFIS-PSO, ANFIS-FFA, ANFIS-GA and ANFIS-GWO, were created to estimate the factor of safety against overturning. The proposed hybrid models were evaluated on two distinct datasets (training 70% and testing 30%) with three input combinations, namely cohesion (c), unit weight of soil (Υ) and angle of shearing resistance (φ). To access the prediction power of different hybrid models, various statistical parameters such as R2, AdjR2, VAF, WI, LMI, a-20 index, PI, KGE, RMSE, SI, MAE, NMBE and MBE were computed for training (TR) and testing (TS) datasets. The overall performance of the models indicated that ANFIS-PSO provided better results among all four models. The reliability index was computed using the first-order second-moment (FOSM) method for all models, and the probability of failure was also computed. A Williams plot was drawn to check the applicability domain of the hybrid model and to check the influence of different input parameters on the prediction of the factor of safety, and the Gini index was also computed.

A reservoir bubble point pressure prediction model using the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique with trend analysis.

The bubble point pressure (Pb) could be obtained from pressure-volume-temperature (PVT) measurements; nonetheless, these measurements have drawbacks such as time, cost, and difficulties associated with conducting experiments at high-pressure-high-temperature conditions. Therefore, numerous attempts have been made using several approaches (such as regressions and machine learning) to accurately develop models for predicting the Pb. However, some previous models did not study the trend analysis to prove the correct relationships between inputs and outputs to show the proper physical behavior. Thus, this study aims to build a robust and more accurate model to predict the Pb using the adaptive neuro-fuzzy inference system (ANFIS) and trend analysis approaches for the first time. More than 700 global datasets have been used to develop and validate the model to robustly and accurately predict the Pb. The proposed ANFIS model is compared with 21 existing models using statistical error analysis such as correlation coefficient (R), standard deviation (SD), average absolute percentage relative error (AAPRE), average percentage relative error (APRE), and root mean square error (RMSE). The ANFIS model shows the proper relationships between independent and dependent parameters that indicate the correct physical behavior. The ANFIS model outperformed all 21 models with the highest R of 0.994 and the lowest AAPRE, APRE, SD, and RMSE of 6.38%, -0.99%, 0.074 psi, and 9.73 psi, respectively, as the first rank model. The second rank model has the R, AAPRE, APRE, SD, and RMSE of 0.9724, 9%, -1.58%, 0.095 psi, and 13.04 psi, respectively. It is concluded that the proposed ANFIS model is validated to follow the correct physical behavior with higher accuracy than all studied models.

ANFIS modelling with fuzzy C-mean clustering of experimentally evaluated thermophysical properties of zirconia-water nanofluids

Experiments on the thermal conductivity, viscosity, density, and specific heat of water based ZrO2 nanofluids were carried out in this study. The XRD and TEM were used to characterize the ZrO2 nanoparticles after they were produced through sol–gel technique. Temperatures and particle volume loadings of 0.2 to 1.0 % and 20 to 60 °C, respectively, were taken into account for the analysis. For 1.0 % of water-based ZrO2 nanofluids, thermal conductivity is increased by 24.96 % at 60 °C, moreover, under the same particle loading of 1.0 %, the viscosity was increased by 45.57 % at 20 °C over the water data. On the other side, the density is increased and the specific heat is decreased with the mixing of nanoparticles to the base fluid. The effective thermophysical properties of nanofluids were modeled using an intelligent model, adaptive neuro-fuzzy inference system (ANFIS) technique. The results of the ANFIS model outperformed the calculated correlations.